Stable Multiple Activity Matching Based Content Sharing For Mobile Crowd Sensing

The emerging mobile crowd sensing has become a new paradigm where a crowd of mobile users utilize their smart devices to conduct complex computation and sensing tasks in mobile social networks, in which the content sharing plays a significant role. In this work, we propose a novel many-to-many content sharing framework by enabling users to exchange information through separate connections with different partners of multiple relationships simultaneously. Under the statistical channel statement information condition, the many-to-many pairing problem is formulated as a Stable Multiple Activity (SMA) matching game and solved by a distributive stable b-matching algorithm in multigraphs. Based on the matching model, we further perform the power allocation scheme to improve system performance. Simulation results demonstrate the superiority of our proposed SMA method with lower complexity.